DESCRIPTION: (Verbatim from the Applicant's Abstract) The proposed project is a revised new R01 project, MH 60350. We propose to: 1) determine whether EEG sleep abnormalities and early onset independently predict lifetime affective morbidity in the offspring of families identified by depressed probands; 2) estimate the additive effects of early onset and abnormal EEG sleep in lifetime risk for depression; 3) establish cohort of offspring at risk for depression for prospective assessment of predictors of affective morbidity. We have compelling evidence that EEG sleep abnormalities cosegregate in families, are associated with increased lifetime risk for major depression, and double the risk of new-onset depression in at-risk relatives. Early onset of depression in the proband independently conferred increased familial risk for depression. These findings hold for two generations of relatives ascertained through unipolar depressed proband identified as part of MH39531 to the PI. By extending our well-characterized two-generation pedigrees to include the third generation we will specify, in a definitive and cost-effective manner, independent sources of familial risk for depression and morbidity associated with early onset and with abnormal sleep physiology. Young adult offspring are an ideal resource to evaluate the cumulative or interactive influence of these distinct sources of risk. Lifetime psychiatric disorders and EEG sleep have been studied in parent and grandparent generations. Offspring targeted for this proposal have not been studies as part of MH39531. Unipolar depressed probands who defined our families had onset of depression before age 45 and 63 percent had their first episode by age 25 (early onset). Data on EEG sleep and clinical history in these offspring can be critically informative in describing physiological characteristics that cosegregate with the disorder and build on observations from two well-characterized generations to identify pathophysiologically important characteristics in adult offspring. Effects of age, sex, illness and medication will be controlled to generate information that is relevant across generations and across time. The proposed study creates a cohort that spans three generations, provides the framework for longitudinal, prospective predictions and identifies maximally informative families who can then be studied using molecular and/or genetic techniques.